From cfb9c31c99611a61090c97f2216c5e54db490227 Mon Sep 17 00:00:00 2001
From: John Pollock <pollockjj@gmail.com>
Date: Thu, 11 Jun 2026 10:41:23 -0500
Subject: [PATCH] Add SeedVR2 sampler coverage

---
 .../test_seedvr_progressive_sampler.py        | 95 +++++++++++++++++++
 1 file changed, 95 insertions(+)
 create mode 100644 tests-unit/comfy_test/test_seedvr_progressive_sampler.py

diff --git a/tests-unit/comfy_test/test_seedvr_progressive_sampler.py b/tests-unit/comfy_test/test_seedvr_progressive_sampler.py
new file mode 100644
index 000000000..146b81225
--- /dev/null
+++ b/tests-unit/comfy_test/test_seedvr_progressive_sampler.py
@@ -0,0 +1,95 @@
+"""Unit tests for ``comfy_extras.nodes_seedvr.SeedVR2ProgressiveSampler``."""
+
+from unittest.mock import patch
+
+import pytest
+import torch
+
+from comfy.cli_args import args as cli_args
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+import comfy.sample  # noqa: E402
+import comfy_extras.nodes_seedvr as nodes_seedvr_mod  # noqa: E402
+from comfy_extras.nodes_seedvr import SeedVR2ProgressiveSampler  # noqa: E402
+
+_LAT_C = 16
+_COND_C = 17
+
+
+def _make_inputs(B: int = 1, T: int = 5, H: int = 8, W: int = 8):
+    """Build minimal SeedVR2-shaped sampling inputs."""
+    samples_5d = torch.arange(
+        B * _LAT_C * T * H * W, dtype=torch.float32
+    ).reshape(B, _LAT_C, T, H, W)
+    samples = samples_5d.reshape(B, _LAT_C * T, H, W).contiguous()
+
+    cond_5d = torch.arange(
+        B * _COND_C * T * H * W, dtype=torch.float32
+    ).reshape(B, _COND_C, T, H, W) + 10000.0
+    cond = cond_5d.reshape(B, _COND_C * T, H, W).contiguous()
+
+    text_pos = torch.zeros(1, 4, 32)
+    text_neg = torch.zeros(1, 4, 32)
+    positive = [[text_pos, {"condition": cond.clone()}]]
+    negative = [[text_neg, {"condition": cond.clone()}]]
+    latent_image = {"samples": samples}
+    return latent_image, positive, negative, samples_5d, cond_5d
+
+
+def _identity_fix_empty(model, latent_image, downscale_ratio_spacial=None):
+    return latent_image
+
+
+def _fingerprinted_prepare_noise(latent_image, seed, batch_inds=None):
+    """Return a tensor whose values encode ``(seed, position)``."""
+    base = torch.arange(
+        latent_image.numel(), dtype=torch.float32
+    ).reshape(latent_image.shape)
+    return base + float(seed) * 1e6
+
+
+def test_progressive_sampler_schema_exposes_manual_default_auto_chunking():
+    schema = SeedVR2ProgressiveSampler.define_schema()
+    inputs = {item.id: item for item in schema.inputs}
+
+    assert inputs["chunking_mode"].options == ["manual", "auto"]
+    assert inputs["chunking_mode"].default == "manual"
+
+
+def test_vram_seed_frames_per_chunk_predicts_4n1_clamped_to_t_pixel():
+    """VRAM chunk-size law: seed = nearest 4n+1 to 4*(free_GB - 3), clamped to [1, t_pixel]."""
+    gib = 1024 ** 3
+    seed = nodes_seedvr_mod._seedvr2_vram_seed_frames_per_chunk
+    assert seed(20 * gib, 65) == 65   # 4*(20-3)=68 -> 4n+1 69 -> clamp to t_pixel 65
+    assert seed(6 * gib, 97) == 13    # 4*(6-3)=12 -> nearest 4n+1 13
+    assert seed(2 * gib, 97) == 1     # below margin -> floor at 1
+
+
+@pytest.mark.parametrize("bad_chunk", [0, -1, 2])
+def test_t3_invalid_frames_per_chunk_raises_value_error(bad_chunk):
+    """``frames_per_chunk`` violating 4n+1 (or <1) must raise ``ValueError`` before any model invocation."""
+    latent, pos, neg, _, _ = _make_inputs(T=5)
+
+    sampler_called = {"n": 0}
+
+    def _should_not_be_called(*args, **kwargs):
+        sampler_called["n"] += 1
+        return torch.zeros(1)
+
+    with patch.object(comfy.sample, "sample",
+                      side_effect=_should_not_be_called), \
+         patch.object(comfy.sample, "fix_empty_latent_channels",
+                      side_effect=_identity_fix_empty), \
+         patch.object(comfy.sample, "prepare_noise",
+                      side_effect=_fingerprinted_prepare_noise):
+        with pytest.raises(ValueError) as excinfo:
+            SeedVR2ProgressiveSampler.execute(
+                model=None, seed=0, steps=2, cfg=1.0,
+                sampler_name="euler", scheduler="simple",
+                positive=pos, negative=neg, latent=latent,
+                denoise=1.0, frames_per_chunk=bad_chunk, temporal_overlap=0,
+            )
+    assert str(bad_chunk) in str(excinfo.value)
+    assert sampler_called["n"] == 0